Recent developments in cloud computing have made it a powerful solution for executing large-scale scientific problems. The complexity of scientific workflows demands efficient utilization of cloud resources to satisfy user requirements. Scheduling of scientific workflows in a cloud environment is a challenge for researchers. The problem is considered as NP-hard. Some constraints such as a heterogeneous environment, dependencies between tasks, quality of service and user deadlines make it difficult for the scheduler to fully utilize available resources. The problem has been extensively studied in the literature. Different researchers have targeted different parameters. This article presents a multi-objective scheduling algorithm for scheduling scientific workflows in cloud computing. The solution is based on genetic algorithm that targets makespan, monetary cost, and load balance. The proposed algorithm first finds the best solution for each parameter. Based on these solutions, the algorithm finds the superbest solution for all parameters. The proposed algorithm is evaluated with benchmark datasets and comparative results with the standard genetic algorithm, particle swarm optimization, and specialized scheduler are presented. The results show that the proposed algorithm achieves an improvement in makespan and reduces the cost with a well load balanced system.

中文翻译：

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一种用于调度云计算科学工作流的并行多目标遗传算法

云计算的最新发展使其成为执行大规模科学问题的强大解决方案。科学工作流的复杂性要求有效利用云资源来满足用户需求。在云环境中安排科学工作流程对研究人员来说是一个挑战。该问题被认为是 NP-hard。诸如异构环境、任务之间的依赖关系、服务质量和用户截止日期等一些限制使调度程序难以充分利用可用资源。该问题已在文献中进行了广泛研究。不同的研究人员针对不同的参数。本文提出了一种用于在云计算中调度科学工作流的多目标调度算法。该解决方案基于针对完工时间的遗传算法，货币成本和负载平衡。所提出的算法首先为每个参数找到最佳解决方案。基于这些解决方案，该算法为所有参数找到最佳解决方案。所提出的算法用基准数据集进行评估，并与标准遗传算法、粒子群优化和专用调度程序进行比较。结果表明，所提出的算法在具有良好负载平衡的系统的情况下实现了完工时间的改进并降低了成本。提出了粒子群优化和专门的调度程序。结果表明，所提出的算法在具有良好负载平衡的系统的情况下实现了完工时间的改进并降低了成本。提出了粒子群优化和专门的调度程序。结果表明，所提出的算法在具有良好负载平衡的系统的情况下实现了完工时间的改进并降低了成本。